Spatial metabolomics for evaluating response to neoadjuvant therapy in non‐small cell lung cancer patients

作者：Jian Xing Shen, Na Sun, Philipp Zens, Thomas Kunzke, Achim Buck, Verena M. Prade, Jun Wang, Qian Wang, Ronggui Hu, Annette Feuchtinger, Sabina Berezowska, Axel Karl Walch · 发表于：癌症：英文版 · 年份：2022 · DOI：10.1002/cac2.12310 · 被引用次数：46 · 研究领域：Metabolomics and Mass Spectrometry Studies、Cancer, Hypoxia, and Metabolism、Immune cells in cancer

BACKGROUND: The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non-small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read-out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning. METHODS: Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high-resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy-naïve patients with NSCLC (n = 85). RESULTS: The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual ...